Dissertation Appendix A
Chapter 1: Agricultural Insurance Loss Analysis of the Pacific Northwest, 2001 to 2015
Appendix A documents the Chapter 1 exploratory data analysis process, examining the relationships of agricultural commodity loss, at a county level, from 1989-2015, across the three state region of the Pacific Northwest (Washington, Idaho, and Oregon), and then focus in on the 24 county region of the Inland Pacific Northwest (IPNW).
We examine the entire range of commodities and damage causes for the IPNW study area, identifying the top revenue loss commodities and their most pertinent damage causes, as indicated from the USDA’s agricultural commodity loss insurance archive. We also explore claim freqency, We fiinally zero in on the top 5 commodities for the region, and examine specific loss variation by year and damage cause.
Several steps were performed to examine the full set of data, and to narrow down factors to a usable form for further modeling analysis.
Step 1. Data Preparation. Here we load our agricultural insurance loss data.
Step 2. Data Preparation. Original Insurance Loss Dataset - Pacific Northwest
Step 3. Data Preparation. Aggregated Insurance Loss Dataset - Pacific Northwest
Step 4. Pacific Northwest (PNW) Overview. Here we examine overall commodity loss for the entire three state region of Washington, Idaho, and Oregon, which comes primarily from three primary agricultural regions - the IPNW (mostly Washington, with portions of northern Idaho and northeastern Oregon), the Southern Idaho Valley (Idaho), and the Willamette Valley (Oregon).
Step 5. PNW region overview of insurance loss by year, damage cause, and commodity, and county: 1989 to 2015. From the results of this examination, we narrow our time frame to 2001 to 2015.
Step 6. PNW region overview of insurance Loss claim counts by year, damage cause, commodity, and county: 2001-2015
Step 7. Refining our analysis to the 24-county inland Pacific Northwest (iPNW) Study Area.
Step 8. IPNW region overview of Insurance Loss by year, damage cause, commodity and county: 2001-2015
Step 9. IPNW region overview of Insurance Loss claim counts: 2001-2015
Step 10. APPLES: IPNW region overview of Insurance Loss 2001-2015
Step 11. BARLEY: IPNW region overview of Insurance Loss 2001-2015
Step 12. WHEAT: IPNW region overview of Insurance Loss 2001-2015
Step 13. CHERRIES: IPNW region overview of Insurance Loss 2001-2015
Step 14. PEAS: IPNW region overview of Insurance Loss 2001-2015
In order to generate any of the following tables and graphs, you will need to download the dataload.R file from the following location:
http://github.com/erichseamon/seamon_dissertation/data/
Run this dataload.R file locally. This will download all data and place it in your /tmp file. Then you will be able to re-generate any of the provided Rmarkdown files in:
http://github.com/erichseamon/seamon_dissertation/appendices/
the dataload.R file has a multitude of datasets, including:
* climatology summary data by county for the study area examined (inland Pacific Northwest);
* original RMA crop insurance data;
* state polygon shapefile;
* consumer price indexing;
* wheat yields; and
* wheat pricing
The following table is an example of the original data that was acquired from the USDA Risk Management Agency (RMA). Each record represents an individual insurance claim.
| year | state | county | commodity | damagecause | monthcode | month | acres | loss | lossperacre | cropyear | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 11338 | 2001 | ID | Ada | All Other Crops | Drought | 9 | SEP | 17.000 | 153.00 | 9.000000 | 2001 |
| 11339 | 2001 | ID | Ada | All Other Crops | Heat | 8 | AUG | 105.200 | 5249.00 | 49.895437 | 2001 |
| 11340 | 2001 | ID | Ada | All Other Crops | Freeze | 4 | APR | 125.000 | 4500.00 | 36.000000 | 2001 |
| 11341 | 2001 | ID | Ada | All Other Crops | Wind/Excess Wind | 5 | MAY | 50.000 | 1800.00 | 36.000000 | 2001 |
| 11342 | 2001 | ID | Ada | All Other Crops | Wind/Excess Wind | 4 | APR | 92.500 | 3330.00 | 36.000000 | 2001 |
| 11343 | 2001 | ID | Bannock | WHEAT | Drought | 8 | AUG | 133.000 | 1212.00 | 9.112782 | 2001 |
| 11344 | 2001 | ID | Bannock | WHEAT | Drought | 9 | SEP | 777.520 | 24807.00 | 31.905289 | 2001 |
| 11345 | 2001 | ID | Bannock | WHEAT | Drought | 7 | JUL | 3529.754 | 54726.46 | 15.504327 | 2001 |
| 11346 | 2001 | ID | Bannock | WHEAT | Heat | 7 | JUL | 19.796 | 2371.60 | 119.801980 | 2001 |
| 11347 | 2001 | ID | Bannock | WHEAT | Heat | 8 | AUG | 25.000 | 904.00 | 36.160000 | 2001 |
The following table is an example of an aggregated dataset derived from the original files. Here we have summarized claims by year, county, commodity, and damage cause. Each unique combination is summarized, which echos the total summarized loss, the number of claims, the total summarized acreage, loss per acre, loss per claim, and acres per claim. This dataset was the basis for our data examination.
| year | state | county | commodity | damagecause | loss | count | acres | lossperacre | lossperclaim | acresperclaim | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 11 | 2014 | ID | Ada | All Other Crops | Area Plan Crops Only | 1398 | 1 | 0 | 0.0000000 | 1398.0 | 0 |
| 12 | 2015 | ID | Ada | All Other Crops | Area Plan Crops Only | 11810 | 1 | 0 | 0.0000000 | 11810.0 | 0 |
| 211 | 2008 | OR | Baker | All Other Crops | Area Plan Crops Only | 15292 | 2 | 5482 | 2.7894929 | 7646.0 | 2741 |
| 212 | 2010 | OR | Baker | All Other Crops | Area Plan Crops Only | 1819 | 2 | 2282 | 0.7971078 | 909.5 | 1141 |
| 215 | 2009 | ID | Bannock | All Other Crops | Area Plan Crops Only | 2284 | 1 | 600 | 3.8066667 | 2284.0 | 600 |
| 245 | 2008 | ID | Bear Lake | All Other Crops | Area Plan Crops Only | 8102 | 1 | 2468 | 3.2828201 | 8102.0 | 2468 |
| 246 | 2012 | ID | Bear Lake | All Other Crops | Area Plan Crops Only | 7459 | 1 | 2468 | 3.0222853 | 7459.0 | 2468 |
| 291 | 2010 | ID | Bingham | All Other Crops | Area Plan Crops Only | 4197 | 1 | 192 | 21.8593750 | 4197.0 | 192 |
| 292 | 2011 | ID | Bingham | All Other Crops | Area Plan Crops Only | 7769 | 1 | 240 | 32.3708333 | 7769.0 | 240 |
| 293 | 2012 | ID | Bingham | All Other Crops | Area Plan Crops Only | 6786 | 1 | 168 | 40.3928571 | 6786.0 | 168 |
The following map shows the three key agricultural regions that make up over 95% of all agricultural insurance loss for the three state region of Washington, Oregon, and Idaho.
Here we examine the PNW region’s insurance loss by year, damage cause, and commodity, and county, from 1989 to 2015. From the results of this examination, we narrow our time frame to 2001 to 2015.
| PNW total insurance loss by year, 1989-2015 | |
| 1989-2015 | |
|---|---|
| damage cause | loss |
| Drought | 814496893.53 |
| Decline in Price | 406959628.09 |
| Heat | 307635976.72 |
| Frost | 235527722.93 |
| Freeze | 208770819.89 |
| Hail | 177157029.63 |
| Excessive Moisture | 165391940.25 |
| Cold Wet Weather | 109229260.25 |
| Cold Winter | 75956007.78 |
| Failure Irrig Supply | 61515359.03 |
| PNW total insurance loss by year, 2001-2015 | |
| 2001-2015 | |
|---|---|
| damage cause | loss |
| Drought | $723,039,146 |
| Decline in Price | $401,633,728 |
| Heat | $276,188,676 |
| Frost | $183,961,154 |
| Freeze | $167,008,613 |
| Excess Moisture/Precip/Rain | $156,789,247 |
| Hail | $150,625,615 |
| Cold Wet Weather | $98,760,529 |
| Cold Winter | $68,763,872 |
| Failure Irrig Supply | $58,570,602 |
| PNW total insurance loss by year, 2001-2015 | |
| 2001-2015 | |
|---|---|
| commodity | loss |
| WHEAT | $1,444,439,251 |
| APPLES | $156,063,380 |
| CHERRIES | $154,524,200 |
| POTATOES | $117,459,400 |
| ADJUSTED GROSS REVENUE | $116,781,042 |
| BARLEY | $102,125,046 |
| DRY PEAS | $58,038,162 |
| All Other Crops | $55,112,560 |
| PNW total insurance loss by year, 2001-2015 | |
| 2001-2015 | |
|---|---|
| year | loss |
| 2001 | $77,233,486 |
| 2002 | $104,897,463 |
| 2003 | $114,966,090 |
| 2004 | $52,018,936 |
| 2005 | $72,875,470 |
| 2006 | $68,982,845 |
| 2007 | $87,943,728 |
| 2008 | $160,537,412 |
| 2009 | $567,460,556 |
| 2010 | $106,794,738 |
| 2011 | $125,625,761 |
| 2012 | $95,809,547 |
| 2013 | $177,199,860 |
| 2014 | $270,956,814 |
| 2015 | $313,739,035 |
Below we examine the PNW insurance claim counts by the same factors: by commodity, damage cause, year, and county.
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| IPNW region total insurance loss by year, 2001-2015 | |
| 2001-2015 | |
|---|---|
| year | loss |
| 2001 | $51,203,203 |
| 2002 | $73,905,306 |
| 2003 | $70,314,803 |
| 2004 | $43,387,412 |
| 2005 | $45,054,042 |
| 2006 | $50,022,732 |
| 2007 | $47,237,969 |
| 2008 | $117,324,601 |
| 2009 | $436,808,728 |
| 2010 | $51,023,916 |
| 2011 | $65,883,241 |
| 2012 | $46,062,127 |
| 2013 | $116,089,278 |
| 2014 | $174,745,874 |
| 2015 | $237,671,277 |
| IPNW region total insurance loss by commodity, 2001-2015 | |
| 2001-2015 | |
|---|---|
| commodity | loss |
| WHEAT | $1,203,744,611 |
| APPLES | $89,186,728 |
| CHERRIES | $74,853,342 |
| DRY PEAS | $55,590,740 |
| BARLEY | $33,476,785 |
| GRAPES | $17,370,434 |
| IPNW region total insurance loss by damage cause, 2001-2015 | |
| 2001-2015 | |
|---|---|
| damage cause | loss |
| Drought | $673,854,997 |
| Decline in Price | $287,278,003 |
| Heat | $197,275,538 |
| Frost | $91,808,517 |
| Hail | $81,776,579 |
| Freeze | $78,563,950 |
| Excessive Moisture | $76,325,319 |
| Cold Wet Weather | $52,762,454 |
| Cold Winter | $33,445,556 |
| Other Causes | $19,094,006 |
| Wind/Excess Wind | $12,268,262 |
| IPNW region APPLES total insurance loss by damage cause, 2001-2015 | |
| 2001-2015 | |
|---|---|
| damagecause | loss |
| Hail | $40,971,994 |
| Frost | $17,016,586 |
| Freeze | $14,534,548 |
| Cold Wet Weather | $9,246,848 |
| Wind/Excess Wind | $2,934,044 |
| Heat | $2,422,480 |
| Other | $2,060,230 |
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## Error in pal(county_apples_hailfrostfreeze$loss): could not find function "pal"
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## Error in pal(county_apples_hailfrostfreeze2013$loss): could not find function "pal"
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| IPNW region BARLEY total insurance loss by damage cause, 2001-2015 | |
| 2001-2015 | |
|---|---|
| damagecause | loss |
| Drought | $19,557,132 |
| Excess Moisture/Precip/Rain | $4,606,318 |
| Heat | $4,409,923 |
| Hail | $1,446,047 |
| Frost | $1,158,127 |
| Other | $936,234 |
| Decline in Price | $848,321 |
| Cold Wet Weather | $514,682 |
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## Error in pal(county_barley_droughtheat2015$loss): could not find function "pal"
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## Error in pal(county_barley_droughtheat$loss): could not find function "pal"
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| IPNW region WHEAT total insurance loss by damage cause, 2001-2015 | |
| 2001-2015 | |
|---|---|
| damagecause | loss |
| Drought | $623,556,410 |
| Decline in Price | $242,258,102 |
| Heat | $149,599,349 |
| Frost | $45,091,012 |
| Excess Moisture/Precip/Rain | $31,923,877 |
| Cold Winter | $27,596,223 |
| Freeze | $27,064,747 |
| Cold Wet Weather | $23,086,130 |
| Hail | $19,621,554 |
| Other | $9,008,705 |
| Plant Disease | $2,925,149 |
| Other (Snow-Lightning-Etc.) | $2,013,353 |
## Error in names(x) <- value: 'names' attribute [4] must be the same length as the vector [3]
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## Error in max(wheatprice_all$price_adjusted):min(wheatprice_all$price): result would be too long a vector
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## Error in rbind(WA_wheat_yields, ID_wheat_yields, OR_wheat_yields): object 'WA_wheat_yields' not found
## Error in aggregate(iPNW_wheat_yields$value, by = list(iPNW_wheat_yields$county), : object 'iPNW_wheat_yields' not found
## Error in colnames(iPNW_wheat_yields_annual_average) <- c("NAME", "ave_yield"): object 'iPNW_wheat_yields_annual_average' not found
## Error in aggregate(iPNW_wheat_yields$value, by = list(iPNW_wheat_yields$year), : object 'iPNW_wheat_yields' not found
## Error in colnames(iPNW_wheat_yields_annual_average_barplot) <- c("Year", : object 'iPNW_wheat_yields_annual_average_barplot' not found
## Error in merge(counties, iPNW_wheat_yields_annual_average, by = c("NAME")): object 'iPNW_wheat_yields_annual_average' not found
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## Error in pal(county_wheat_droughtheat2009$loss): could not find function "pal"
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## Error in pal(county_wheat_droughtheat2015$loss): could not find function "pal"
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## Error in rbind(WA_wheat_yields, ID_wheat_yields, OR_wheat_yields): object 'WA_wheat_yields' not found
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## Error in colnames(iPNW_wheat_yields_annual_average) <- c("NAME", "ave_yield"): object 'iPNW_wheat_yields_annual_average' not found
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## Error in colnames(iPNW_wheat_yields_annual_average_barplot) <- c("Year", : object 'iPNW_wheat_yields_annual_average_barplot' not found
## Error in merge(counties, iPNW_wheat_yields_annual_average, by = c("NAME")): object 'iPNW_wheat_yields_annual_average' not found
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## Error in pal(WHEAT_damage_droughtheat_annual_total_map$loss): could not find function "pal"
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| IPNW region CHERRIES total insurance loss by damage cause, 2001-2015 | |
| 2001-2015 | |
|---|---|
| damagecause | loss |
| Excess Moisture/Precip/Rain | $20,569,890 |
| Frost | $15,459,348 |
| Freeze | $14,625,022 |
| Decline in Price | $8,447,020 |
| Cold Wet Weather | $6,724,754 |
| Heat | $2,246,093 |
| Hail | $2,034,613 |
| Other (Snow-Lightning-Etc.) | $1,876,035 |
| Other | $1,467,713 |
| Wind/Excess Wind | $1,402,854 |
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## Error: 'decode_colour' is not an exported object from 'namespace:farver'
## Error in pal(county_cherries_moisturefrostfreeze$loss): could not find function "pal"
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## Error in pal(county_cherries_moisturefrostfreeze2013$loss): could not find function "pal"
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## Error in pal(county_cherries_moisturefrostfreeze2015$loss): could not find function "pal"
## Error in getMapData(map): object 'map' not found
| IPNW region DRY PEAS total insurance loss by damage cause, 2001-2015 | |
| 2001-2015 | |
|---|---|
| damagecause | loss |
| Excessive Moisture | $22,316,191 |
| Heat | $10,205,657 |
| Excess Moisture/Precip/Rain | $8,046,188 |
| Hail | $5,171,205 |
| Decline in Price | $2,924,900 |
| Cold Wet Weather | $2,867,390 |
| Other | $2,064,864 |
| Wind/Excess Wind | $1,079,927 |
| Frost | $914,417 |
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## Error: 'decode_colour' is not an exported object from 'namespace:farver'
## Error in pal(county_drypeas_droughtheat2001_2015$loss): could not find function "pal"
## Error in getMapData(map): object 'map' not found
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## Error in pal(county_drypeas_excessivemoisture2011$loss): could not find function "pal"
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## Error in pal(county_drypeas_droughtheat2015$loss): could not find function "pal"
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